"""resnet in pytorch
    https://arxiv.org/abs/1512.03385v1
"""

import torch.nn as nn


class BasicBlock(nn.Module):
    """
    Basic Block for resnet 18 and resnet 34
    """
    expansion = 1

    def __init__(self, in_channels, out_channels, stride=1):
        super(BasicBlock, self).__init__()

        self.residual_function = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(out_channels)
        )
        # shortcut
        self.shortcut = nn.Sequential()
        self.relu = nn.ReLU(inplace=True)

        if stride != 1 or in_channels != out_channels * BasicBlock.expansion:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_channels, out_channels * BasicBlock.expansion, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(out_channels * BasicBlock.expansion)
            )

    def forward(self, x):
        out = self.residual_function(x)
        identity = self.shortcut(x)
        x = out + identity
        return self.relu(x)


class BottleNeck(nn.Module):
    """
    Residual block for resnet over 50 layers
    """
    expansion = 4

    def __init__(self, in_channels, out_channels, stride=1):
        super().__init__()
        self.residual_function = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels, stride=stride, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels * BottleNeck.expansion, kernel_size=1, bias=False),
            nn.BatchNorm2d(out_channels * BottleNeck.expansion),
        )

        self.shortcut = nn.Sequential()
        self.relu = nn.ReLU(inplace=True)
        if stride != 1 or in_channels != out_channels * BottleNeck.expansion:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_channels, out_channels * BottleNeck.expansion, stride=stride, kernel_size=1, bias=False),
                nn.BatchNorm2d(out_channels * BottleNeck.expansion)
            )

    def forward(self, x):
        out = self.residual_function(x)
        identity = self.shortcut(x)
        x = out + identity
        return self.relu(x)


class ResNet(nn.Module):

    def __init__(self, block, num_block, num_class=100):
        super().__init__()

        self.in_channels = 64

        self.conv1 = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(3, stride=2, padding=1)
        )

        self.conv2_x = self._make_layer(block, 64, num_block[0], 1)
        self.conv3_x = self._make_layer(block, 128, num_block[1], 2)
        self.conv4_x = self._make_layer(block, 256, num_block[2], 2)
        self.conv5_x = self._make_layer(block, 512, num_block[3], 2)
        self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * block.expansion, num_class)

    def _make_layer(self, block, out_channels, num_blocks, stride):
        """
        制作resnet层（我所说的“层”并不是指这个“层”与神经元网络层相同，例如conv层），一层可能包含多个残差块
        Args：
            block：阻滞型、基本阻滞或瓶颈阻滞
            out_channels：该层的输出深度通道号
            num_blocks：每层有多少块
            stride：该层第一块的步幅
            返回：
            返回resnet layer
        """
        # 我们每层有num_block块，第一个块为1或2，其他块始终为1
        strides = [stride] + [1] * (num_blocks - 1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_channels, out_channels, stride))
            self.in_channels = out_channels * block.expansion
        return nn.Sequential(*layers)

    def forward(self, x):
        output = self.conv1(x)
        output = self.conv2_x(output)
        output = self.conv3_x(output)
        output = self.conv4_x(output)
        output = self.conv5_x(output)
        output = self.avg_pool(output)
        output = output.view(output.size(0), -1)
        output = self.fc(output)

        return output


def resnet18(num_class=1000):
    """ return a ResNet 18 object
    """
    return ResNet(BasicBlock, [2, 2, 2, 2], num_class=num_class)


def resnet34(num_class=1000):
    """ return a ResNet 34 object
    """
    return ResNet(BasicBlock, [3, 4, 6, 3], num_class=num_class)


def resnet50(num_class=1000):
    """ return a ResNet 50 object
    """
    return ResNet(BottleNeck, [3, 4, 6, 3], num_class=num_class)


def resnet101(num_class=1000):
    """ return a ResNet 101 object
    """
    return ResNet(BottleNeck, [3, 4, 23, 3], num_class=num_class)


def resnet152(num_class=1000):
    """ return a ResNet 152 object
    """
    return ResNet(BottleNeck, [3, 8, 36, 3], num_class=num_class)
